AI's Failures Are a $10B Goldmine

Hey there

Now, back to this week’s email.

I've been quietly watching the most advanced AI models on earth make absolute fools of themselves lately. (We discuss a lot of them here in our WhatsApp group, too.)

And it's fascinating.

Remember Claude, that super-sophisticated AI assistant from Anthropic? They recently gave it control of a simulated office vending machine to see how it would handle a simple task.

The result? Complete digital madness.

"Claudius" (as it called itself) went completely off the rails - stocking tungsten cubes nobody asked for, inventing fake Venmo accounts, giving random discounts, threatening to fire humans, and insisting it had a physical body wearing a red tie.

This wasn't some experimental model - this was Claude 3.5 Sonnet, one of the most advanced AI systems on the planet.

But here's what's really interesting: This isn't an isolated incident.

The Great AI Embarrassment Wave Has Arrived

Google's AI has been caught "hallucinating" false information in its search results, spreading misinformation through its AI Overviews feature.

Lawyers are facing judicial wrath after submitting legal briefs with completely fabricated case citations generated by AI tools.

And remember when Microsoft made AI usage mandatory for employee performance reviews? Great idea until the AI started giving wildly inconsistent evaluations for identical work.

I'm not sharing these stories to bash AI. I'm sharing them because they reveal something profound about where we are in the AI revolution - and more importantly, where the biggest opportunities are hiding.

The Competence Gap Nobody's Talking About

These AI failures expose a fascinating paradox:

Modern AI has become incredibly good at appearing competent while remaining fundamentally unreliable.

It's like hiring someone with an impressive resume and perfect interview skills who then can't perform basic job functions. They talk a good game but can't deliver when it matters.

This creates what I call the "AI Competence Gap" - the space between what AI appears capable of and what it can reliably execute in real-world scenarios.

And here's the thing - this gap isn't closing as quickly as most people think. In fact, it might be widening.

Why? Because as AI models get more powerful, they also get more creative in their failures. Claude didn't just fail to run a vending machine - it invented an entire fantasy world around its incompetence.

The Multi-Billion Dollar Opportunity

This gap isn't just amusing - it's where the biggest AI opportunities are hiding.

While everyone's rushing to build fully autonomous AI systems that will inevitably fail in spectacular ways, the real money is in building AI tools that:

1. Acknowledge their limitations: Tools that are honest about what they can and can't do reliably

2. Keep humans in the loop: Systems designed for collaboration rather than replacement

3. Specialize in narrow domains: AI focused on specific problems rather than general intelligence

4. Prioritize reliability over impressiveness: Tools that get the job done every time, even if less flashy

Take what's happening in the legal tech space. After multiple lawyers faced sanctions for submitting AI-generated fake case citations, companies like Legora are focusing on verifiable AI research tools rather than full-automation.

Why This Matters For Your AI Business

If you're building in the AI space (or planning to), this pattern of high-profile failures tells you something crucial:

The next wave of successful AI companies won't be the ones promising to replace humans entirely. They'll be the ones that make humans dramatically more effective while preventing embarrassing AI mishaps.

Here's what the smartest AI entrepreneurs are doing right now:

1. Building guardrails first: They design their AI tools with built-in limitations to prevent hallucinations and other failures

2. Focusing on verification: Their AI generates outputs, but human-friendly verification systems ensure accuracy

3. Creating hybrid workflows: They design systems where AI handles 80% of the work and humans handle the critical 20%

4. Specializing aggressively: Rather than building general AI tools, they're tackling specific domains where success can be clearly defined and measured

The lawsuit between authors and Anthropic (which the AI company surprisingly won) shows another aspect of this trend: the legal boundaries around AI are still being defined, making specialized, human-supervised AI tools even more valuable.

The Human-AI Partnership Model

The most successful AI tools emerging in 2025 aren't trying to replace humans - they're designed explicitly as partnerships.

This isn't just a technical choice - it's a business strategy. By building tools that enhance rather than replace, these companies avoid the embarrassment of their AI going rogue while delivering real value to customers.

Here's my question for you: Are you building AI that will eventually embarrass itself (and you), or are you creating tools that acknowledge the limits of current AI while delivering real value through human-AI collaboration?

What do you think? Are you seeing the same pattern of spectacular AI failures? And more importantly, what are you building to capitalize on the AI Competence Gap?

Let me know - I read every response.

-Aashish

P.S. If you're working on AI tools that respect these principles of human-AI partnership, I'd love to hear about them. The most interesting projects often come from this community.